What is Object Detection in AI Video Monitoring?

Object detection in AI Video Monitoring is a computer vision technique for identifying and locating objects within an image or video. It involves classifying objects into predefined categories and determining their positions, allowing for the recognition and spatial understanding of various objects in a scene.

Key Aspects of Object Detection:

• Classification and Localisation: Object detection classifies objects (e.g., identifying a car, person, or dog) and localises them within the video stream by drawing bounding boxes around detected objects.
• Applications: Object detection is widely used in various fields, such as autonomous driving, healthcare, retail analytics and video surveillance.
• Techniques: Modern object detection techniques often use deep learning models, such as: YOLO (You Only Look Once) – A fast and efficient real-time object detection algorithm.
  SSD (Single Shot MultiBox Detector) – Optimised for speed and accuracy and Faster R-CNN – known for high accuracy in detecting objects.
• Challenges: Dealing with varying object sizes, occlusions, and complex backgrounds can make detection difficult. Environmental factors such as lighting conditions and camera angles also impact accuracy.

Object Detection Role in AI Video Monitoring:

• Enhanced Security: AI-driven object detection helps identify intruders, unauthorised vehicles, and other security threats.
• False Alarm Reduction: AI filters out non-threatening movements such as shadows, animals, and weather conditions.
• Proactive Monitoring: AI-powered analytics continuously analyse video feeds, providing real-time alerts for security personnel to take immediate action.